Research Projects

RNA sequencing analysis

Metabolic engineering in yeasts has traditionally been focused on model organisms such as baker’s yeast. Our work focuses on studying non-model organisms that offer unique advantages for producing valuable compounds. I have used RNA sequencing analysis as a tool to explore the metabolism of various oil-producing yeasts like Rhodosporidium toruloides, Lipomyces starkeyi, and methane-consuming bacteria like Methylococcus capsulatus. These analyses have build on our understanding of these microbes by advancing our knowledge of gene functions and led us to identify new opportunities for genetic engineering.

Genome sequencing and mutation analysis

Our lab uses metabolic engineering techniques such as directed evolution and integration of DNA fragments to improve the growth characteristics of microbes. In this work, I have designed data analysis pipelines to utilize DNA sequencing for the identification of copy number variations, DNA integration sites, point mutations, and other structural variations in the yeast Yarrowia lipolytica.

Sequence alignment and phylogeny

Simultaneous co-fermentation of glucose and xylose, the two most abundant sugars in cellulosic biomass, is a key trait for efficient production of biofuels and chemicals. In this study, we use bioinformatics analysis to identify two structurally distant transporters, which can facilitate partial and complete co-transportation of glucose and xylose. This work contributes to the understanding of the sugar co-transport mechanism in yeast and plant, and enables rapid and efficient co-utilization of cellulosic sugars.

Mathematical modeling of storage metabolism

Storage metabolism is the cellular response to the presence of excess carbon while other nutrients (like nitrogen, phosphorus) are limiting. Bacteria store excess carbon as glycogen and polyhydroxybutyrate. Yeasts and human cells store it as glycogen or lipids. While storage is not essential for cell survival, the ability to accumulate storage lipids influences long-term survival. Storage metabolites can also be used by the cell to cope with starvation conditions, store toxins inside organelles such as lipid bodies, or maintain cellular homeostasis.
In this work, we design a dynamic model to explain the cellular strategy behind storage metabolism and attempt to quantify the benefit of storage. It uses existing allocation-based models alongside multi-omic data to generate model parameters. Once established, it can be further used to guide experimental design for optimal lipid production.